PyMC is a Bayesian probabilistic programming framework used for building probabilistic models and performing Bayesian inference. It provides a probabilistic graphical model library for specifying random variables, priors, and likelihood functions, supported by an MCMC sampling engine and variational inference tools to estimate posterior distributions. The framework features a GPU-accelerated inference backend that compiles models into machine code to increase execution speed. It utilizes a backend-agnostic tensor execution model and just-in-time graph compilation to optimize the computation o
Python Toolkit for Causal and Probabilistic Reasoning
Fast, flexible and easy to use probabilistic modelling in Python.
Probabilistic programming powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
Exploratory analysis of Bayesian models with Python
arviz-devs/arviz 的主要功能包括：Probabilistic Modeling, Python Projects, Statistical Modeling。
arviz-devs/arviz 的开源替代品包括： pymc-devs/pymc — PyMC is a Bayesian probabilistic programming framework used for building probabilistic models and performing Bayesian… pyro-ppl/numpyro — Probabilistic programming powered by JAX for autograd and JIT compilation to GPU/TPU/CPU. stan-dev/pystan — PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io. pgmpy/pgmpy — Python Toolkit for Causal and Probabilistic Reasoning. jmschrei/pomegranate — Fast, flexible and easy to use probabilistic modelling in Python. accord-net/framework — This project is a scientific computing framework for the .NET ecosystem, providing a comprehensive suite of libraries…